The purpose of this study is to design a “complete communities” methodology and apply it to a sub-geography. A complete community ranking weights selected amenities, their relative value, and their distances from an origin point across several types of transportation. Ultimately, a score for each sub-geography is determined. The process for selecting these characteristics and their application will be discussed in more detail below.
The first step in designing the complete communities methodology is determining the geographical region and granularity. In this case, Redwood City was chosen as the sub-geography of interest, broken down at the census block level. This was done to avoid the large distortions that can occur in the isochrones if census block groups are chosen. The census blocks were imported and filtered down to Redwood City, with the final file being saved in an RDS format to avoid redundancies and re-runs. This was the first modification to the suggested methodology outlined.
From here, the points-of-interest (POIs) were imported from OpenStreetMap (OSM). Due to the large number of possible OSM POIs for the Bay Area, select variables were chosen for the complete communities analysis. The selection was based on a survey completed in the Kansas City Area, where respondents outlined amenities they “would like to see more of” in their communities (Quality of Life, 2019). POIs corresponding to each of the response groups were taken, and cross-referenced with the available data for the Bay Area POIs. The exact survey responses can be seen in the references, but respondents mainly stated amenities such as parks, recreational facilities, trails, restaurants, theaters, learning spaces, and restaurants. In addition, the calculation criteria for “Walk Score” was consulted, which is similar to a complete community score but solely for walking. Walk Score amenities like grocery stores, schools, parks, restaurants, and retail were included in this completeness score (Walk Score, 2022).
The selected variables from the OSM POIs were parks, supermarkets, banks, pharmacies, schools, doctors offices, restaurants, pubs, bars, pitches, playgrounds, hospitals, and theaters. This is a more well-rounded group of variables and represents the second deviation from the suggested methodology, as only 5 variables were chosen in the suggested analysis. The POI’s were imported and filtered once, then the files were saved in an RDS format to avoid redundancies and re-runs.
To supplement the primary analysis, the complete communities analysis was repeated for critical amenity – that being, hospitals. This represents the third modification from the suggested analysis and will be based on “minimum access”. Similar to the above methodology, hospital POIs were imported and filtered to the Bay Area. Once again, the files were saved in an RDS format to avoid redundancies and re-runs. The results for the critical amenity analysis will come after the complete communites analysis.
A map of the selected complete community POIs can be seen below.